The cloud data company aims to attract customers who want to build their own artificial intelligence agents
Category: robotics/AI – Page 2
Machine learning models have seeped into the fabric of our lives, from curating playlists to explaining hard concepts in a few seconds. Beyond convenience, state-of-the-art algorithms are finding their way into modern-day medicine as a powerful potential tool. In one such advance, published in Cell Systems, Stanford researchers are using machine learning to improve the efficacy and safety of targeted cell and gene therapies by potentially using our own proteins.
Most human diseases occur due to the malfunctioning of proteins in our bodies, either systematically or locally. Naturally, introducing a new therapeutic protein to cure the one that is malfunctioning would be ideal.
Although nearly all therapeutic protein antibodies are either fully human or engineered to look human, a similar approach has yet to make its way to other therapeutic proteins, especially those that operate in cells, such as those involved in CAR-T and CRISPR-based therapies. The latter still runs the risk of triggering immune responses. To solve this problem, researchers at the Gao Lab have now turned to machine learning models.
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No image is infinitely sharp. For 150 years, it has been known that no matter how ingeniously you build a microscope or a camera, there are always fundamental resolution limits that cannot be exceeded in principle. The position of a particle can never be measured with infinite precision; a certain amount of blurring is unavoidable. This limit does not result from technical weaknesses, but from the physical properties of light and the transmission of information itself.
TU Wien (Vienna), the University of Glasgow and the University of Grenoble therefore posed the question: Where is the absolute limit of precision that is possible with optical methods? And how can this limit be approached as closely as possible?
And indeed, the international team succeeded in specifying a lowest limit for the theoretically achievable precision and in developing AI algorithms for neural networks that come very close to this limit after appropriate training. This strategy is now set to be employed in imaging procedures, such as those used in medicine. The study is published in the journal Nature Photonics.
With the Darwin-Gödel Machine (DGM), Sakana AI introduces an AI system that can iteratively improve itself through self-modification and open-ended exploration. Early results look promising, but the method is still expensive to run.
Yoshua Bengio, an artificial intelligence pioneer, is creating a new nonprofit research organization to promote an alternative approach to developing cutting-edge AI systems, with the aim of mitigating the technology’s potential risks. The nonprofit, called LawZero, is set to launch Tuesday with $30 million in backing from one of former Google Chief Executive Officer Eric Schmidt’s philanthropic organizations and Skype co-founder Jaan Tallinn, among others. Bengio will lead a team of more than 1
I saw how the e-commerce giant manufactures its warehouse robots during a recent visit to Amazon Robotics.
Most people assume it will take years for AI to reshape the job landscape. But tasks that chatbots can do are already vanishing from job postings.
Recent safety tests show some AI models are capable of sabotaging commands or even resorting to blackmail to avoid being turned off or replaced.